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A novel well log data imputation methods with CGAN and swarm intelligence optimization

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  • Qu, Fengtao
  • Liao, Hualin
  • Liu, Jiansheng
  • Wu, Tianyu
  • Shi, Fang
  • Xu, Yuqiang

Abstract

Well log data plays a vital role in decision-making, resource assessment, production optimization, and environmental management of oil and gas development. However, when exploring and developing deep and ultra-deep oil and gas, the performance of logging equipment is greatly challenged by high temperature, high pressure, and high corrosion wellbore conditions. Log data often needs to be completed or corrected. Data imputation technology has become a powerful tool to fill the missing in well log data. A well log data imputation method based on deep learning is proposed. The proposed method combines conditional generative adversarial networks (CGAN) with swarm intelligence optimization algorithms. The seismic layer velocity is used as a constraint to guide CGAN to generate well log data that matches geological features. Establish an objective function based on multiple conditions to evaluate the quality of generated samples. The swarm intelligence optimization algorithm is used to minimize the objective function. The case study shows that the proposed method obtains pseudo samples that meet specific scenarios and perform better than other algorithms. The proposed method provides a new approach for deep learning algorithms in sequence data prediction.

Suggested Citation

  • Qu, Fengtao & Liao, Hualin & Liu, Jiansheng & Wu, Tianyu & Shi, Fang & Xu, Yuqiang, 2024. "A novel well log data imputation methods with CGAN and swarm intelligence optimization," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224004663
    DOI: 10.1016/j.energy.2024.130694
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    References listed on IDEAS

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    1. Liu, Tao & Tang, Haoran & Wu, Peng & Wang, Haijun & Song, Yuanxin & Li, Yanghui, 2023. "Acoustic characteristics on clayey-silty sediments of the South China Sea during methane hydrate formation and dissociation," Energy, Elsevier, vol. 282(C).
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    3. Sun, Chuan & Chen, Yueyi & Cheng, Cheng, 2021. "Imputation of missing data from offshore wind farms using spatio-temporal correlation and feature correlation," Energy, Elsevier, vol. 229(C).
    4. Chen, Yunxiao & Bai, Mingliang & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2023. "Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting," Energy, Elsevier, vol. 284(C).
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